Box 1: Cognitive Service User

Ensure that the members of a group named Management-Accountants can approve the FAQs.

Approve=publish.

Cognitive Service User (read/write/publish): API permissions: All access to Cognitive Services resource except for ability to:

1. Add new members to roles.

2. Create new resources.

Box 2: Cognitive Services QnA Maker Editor

Ensure that the members of a group named Consultant-Accountants can create and amend the FAQs.

QnA Maker Editor: API permissions:

1. Create KB API

2. Update KB API

3. Replace KB API

4. Replace Alterations

5. "Train API" [in new service model v5]

Box 3: Cognitive Services QnA Maker Read

Ensure that the members of a group named the Agent-CustomerServices can browse the FAQs.

QnA Maker Read: API Permissions:

1. Download KB API

2. List KBs for user API

3. Get Knowledge base details

4. Download Alterations

Generate Answer

Video Indexer supports multiple Person models per account. Once a model is created, you can use it by providing the model ID of a specific Person model when uploading/indexing or reindexing a video. Training a new face for a video updates the specific custom model that the video was associated with.

Note: Video Indexer supports face detection and celebrity recognition for video content. The celebrity recognition feature covers about one million faces based on commonly requested data source such as IMDB, Wikipedia, and top LinkedIn influencers. Faces that aren't recognized by the celebrity recognition feature are detected but left unnamed. Once you label a face with a name, the face and name get added to your account's Person model. Video Indexer will then recognize this face in your future videos and past videos.

For Azure AI Agent Service, the documented quota for the maximum total size of all files uploaded for agents has been listed as 100 GB. Individual files are limited to 512 MB each. These limits are relevant when you plan an agent that must combine and process multiple user-uploaded files.

(Note: Quotas can evolve; check the current “Quotas and limits” page for your region.)

References (Microsoft Docs / Q&A):

Microsoft Q&A (Agent Service limits: “Max size for all uploaded files for agents 100 GB”; per-file 512 MB). Microsoft Learn

File Search tool how-to (per-file 512 MB guidance). Microsoft Learn

 Provide access to AI1 by using: A bearer token

 Connect to the deployment by using: A deployment name

To ensure only your apps can access the Azure OpenAI resource, authenticate with Microsoft Entra ID (Azure AD) and obtain a Bearer token (ideally via managed identity). This avoids sharing long-lived API keys and lets you scope access by app identity and Azure RBAC. Microsoft’s guidance recommends token-based auth for Azure OpenAI and shows how to acquire a bearer token/managed identity token for calls

When calling the REST API, Azure OpenAI requires you to specify the deployment name to select which model deployment to use. The REST route includes {deployment-id} (deployment name) in the path, e.g., .../openai/deployments/{deployment-id}/chat/completions. This is explicitly documented as a key difference from OpenAI’s public API, which takes only a model.

Therefore:

Access method: Bearer token (via Entra ID/managed identity)

Deployment selector: Deployment name in the REST path

Microsoft References

Authenticate to Azure OpenAI with Microsoft Entra ID / Managed identity (bearer token): guidance and how-to

Azure OpenAI request header recommends token-based authentication (Bearer).

Azure OpenAI requires deployment name in API calls; REST path uses

C: Analyze Receipt - Get Analyze Receipt Result.

Query the status and retrieve the result of an Analyze Receipt operation.

Request URL: https://{endpoint}/formrecognizer/v2.0-preview/prebuilt/receipt/analyzeResults/{resultId}

E: POST {Endpoint}/vision/v3.2/read/analyze

Use this interface to get the result of a Read operation, employing the state-of-the-art Optical Character Recognition (OCR) algorithms optimized for text-heavy documents.

Scenario: Contoso plans to develop a document processing workflow to extract information automatically from PDFs and images of financial documents

The document processing solution must be able to process standardized financial documents that have the following characteristics:

- Contain fewer than 20 pages.

- Be formatted as PDF or JPEG files.

- Have a distinct standard for each office.

*The document processing solution must be able to extract tables and text from the financial documents.

The document processing solution must be able to extract information from receipt images.

Can search content in different formats, including video

Audio and video insights (multi-channels). When indexing by one channel, partial result for those models will be available.

Keywords extraction: Extracts keywords from speech and visual text.

Named entities extraction: Extracts brands, locations, and people from speech and visual text via natural language processing (NLP).

Topic inference: Makes inference of main topics from transcripts. The 2nd-level IPTC taxonomy is included.

Artifacts: Extracts rich set of "next level of details" artifacts for each of the models.

Sentiment analysis: Identifies positive, negative, and neutral sentiments from speech and visual text.

 Stream processing has access to the most recent data received or data within a rolling time window. → Yes

 Batch processing must occur immediately and have latency in the order of seconds or milliseconds. → No

 Stream processing is used for simple response functions, aggregates, or calculations such as rolling averages. → Yes

Stream processing has access to the most recent data received or data within a rolling time window.

Correct.

Stream processing handles real-time or near real-time data.

It operates on a continuous data stream and often uses a rolling time window for analytics.

Answer: Yes

Batch processing must occur immediately and have latency in the order of seconds or milliseconds.

Incorrect.

Batch processing is designed for large volumes of data collected over time.

It typically has high latency (minutes, hours, or even days), not immediate execution.

Answer: No

Stream processing is used for simple response functions, aggregates, or calculations such as rolling averages.

Correct.

Common stream analytics tasks include event-driven responses, real-time aggregations, anomaly detection, and rolling averages.

Answer: Yes

Comprehensive Detailed Explanation

Correct Answers:

Yes

No

Yes

Batch vs. Stream processing in Azure

Azure Stream Analytics overview

Microsoft References